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Paper Citation Record · LEDGER

Noise Consistency Regularization for Improved Subject-Driven Image Synthesis

As of 8 August 2026, this Paper Citation Record lists 68 of 68 outbound references and 0 inbound Pith citation observations for arXiv:2506.06483.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2506.06483 v1

Coverage vector

measured 68 of 68 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T05:59:47.743134Z

measured 68 of 68 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

68 of 68 outbound references displayed

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External citation measurements

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Outbound references

Observation 865182b0-9209-4165-a413-7350f0587e7b · outbound

This paper cites Break-a-scene: Extracting multi- ple concepts from a single image.

Noise Consistency Regularization for Improved Subject-Driven Image Synthesis Break-a-scene: Extracting multi- ple concepts from a single image

Reference 1

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Observation da9e20e5-fd69-4ecd-89eb-08efc561d50c · outbound

This paper cites Improving image generation with better captions.Computer Science.

Noise Consistency Regularization for Improved Subject-Driven Image Synthesis Improving image generation with better captions.Computer Science

Reference 2

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation 995ab86c-90b7-454a-b2e8-1b79ade6f371 · outbound

This paper cites Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets.

Noise Consistency Regularization for Improved Subject-Driven Image Synthesis Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets

Reference 3

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Observation afa7f4c6-5e16-4bb5-b1cc-411418ea68ec · outbound

This paper cites Align your latents: High-resolution video synthesis with la- tent diffusion models.

Noise Consistency Regularization for Improved Subject-Driven Image Synthesis Align your latents: High-resolution video synthesis with la- tent diffusion models

Reference 4

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Observation 2065d29b-e297-4c28-9331-b7309dd8b771 · outbound

This paper cites Emerg- ing properties in self-supervised vision transformers.

Noise Consistency Regularization for Improved Subject-Driven Image Synthesis Emerg- ing properties in self-supervised vision transformers

Reference 5

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation c3712f10-5504-470b-a3ff-dff63c008640 · outbound

This paper cites Muse: Text-To-Image Generation via Masked Generative Transformers.

Noise Consistency Regularization for Improved Subject-Driven Image Synthesis Muse: Text-To-Image Generation via Masked Generative Transformers

Reference 6

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Observation b9f440f2-6516-494f-8d07-9074e801c71d · outbound

This paper cites Disenbooth: Identity- preserving disentangled tuning for subject-driven text-to- image generation.

Noise Consistency Regularization for Improved Subject-Driven Image Synthesis Disenbooth: Identity- preserving disentangled tuning for subject-driven text-to- image generation

Reference 7

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation a51f9290-980c-4ba5-88f6-17eaf3ba67ce · outbound

This paper cites Adaptformer: Adapting vision transformers for scalable visual recogni- tion.Advances in Neural Information Processing Systems, 35:16664–16678, 2022.

Noise Consistency Regularization for Improved Subject-Driven Image Synthesis Adaptformer: Adapting vision transformers for scalable visual recogni- tion.Advances in Neural Information Processing Systems, 35:16664–16678, 2022

Reference 8

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Observation 34089ac1-4657-4546-99e2-6e1bf1165e0f · outbound

This paper cites Sem-gan: Semantically- consistent image-to-image translation.

Noise Consistency Regularization for Improved Subject-Driven Image Synthesis Sem-gan: Semantically- consistent image-to-image translation

Reference 9

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation c902dc7d-13a0-4c98-9e3d-dd1b32222e76 · outbound

This paper cites Imagenet: A large-scale hierarchical image database.

Noise Consistency Regularization for Improved Subject-Driven Image Synthesis Imagenet: A large-scale hierarchical image database

Reference 10

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Observation 3172e2d3-7e18-4f9b-bbe5-11fe5ade69ee · outbound

This paper cites Qlora: Efficient finetuning of quantized llms.

Noise Consistency Regularization for Improved Subject-Driven Image Synthesis Qlora: Efficient finetuning of quantized llms

Reference 11

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Observation 5ad1cff7-1e68-4815-960b-467b2ccae30d · outbound

This paper cites KronA: Parameter Efficient Tuning with Kronecker Adapter.

Noise Consistency Regularization for Improved Subject-Driven Image Synthesis KronA: Parameter Efficient Tuning with Kronecker Adapter

Reference 12

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Observation e1756fe4-c114-43a4-97d4-86d23fc28d0b · outbound

This paper cites Gradient- free textual inversion.

Noise Consistency Regularization for Improved Subject-Driven Image Synthesis Gradient- free textual inversion

Reference 13

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation 2efc52da-e956-42c2-aaff-503265f6ec7f · outbound

This paper cites An image is worth one word: Personalizing text-to-image gener- ation using textual inversion.

Noise Consistency Regularization for Improved Subject-Driven Image Synthesis An image is worth one word: Personalizing text-to-image gener- ation using textual inversion

Reference 14

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation 0a1de65e-9744-48ba-ada1-9966efd00004 · outbound

This paper cites Encoder-based Domain Tuning for Fast Personalization of Text-to-Image Models.

Noise Consistency Regularization for Improved Subject-Driven Image Synthesis Encoder-based Domain Tuning for Fast Personalization of Text-to-Image Models

Reference 15

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Observation 40c32239-f5ea-4321-b1a1-4980accebd0c · outbound

This paper cites Encoder-based domain tuning for fast personalization of text-to-image models.ACM Transactions on Graphics (TOG), 42(4):1–13, 2023.

Noise Consistency Regularization for Improved Subject-Driven Image Synthesis Encoder-based domain tuning for fast personalization of text-to-image models.ACM Transactions on Graphics (TOG), 42(4):1–13, 2023

Reference 16

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation b7c63190-06c6-45be-8e7f-b56a6f1c59f1 · outbound

This paper cites Generative adversarial nets.Advances in neural information processing systems, 27, 2014.

Noise Consistency Regularization for Improved Subject-Driven Image Synthesis Generative adversarial nets.Advances in neural information processing systems, 27, 2014

Reference 17

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Observation 7eac35a2-9443-4130-93c4-0181ab0c4889 · outbound

This paper cites Svdiff: Compact param- eter space for diffusion fine-tuning.

Noise Consistency Regularization for Improved Subject-Driven Image Synthesis Svdiff: Compact param- eter space for diffusion fine-tuning

Reference 18

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation 3f572865-7da5-4ad7-a65c-7d869ca8fe53 · outbound

This paper cites LoRA+: Efficient Low Rank Adaptation of Large Models.

Noise Consistency Regularization for Improved Subject-Driven Image Synthesis LoRA+: Efficient Low Rank Adaptation of Large Models

Reference 19

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Observation be7542e2-9dc1-408e-9a74-e849a3238d67 · outbound

This paper cites Lora: Low- rank adaptation of large language models.

Noise Consistency Regularization for Improved Subject-Driven Image Synthesis Lora: Low- rank adaptation of large language models

Reference 20

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T05:59:48.530381Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation 9d2990f5-95eb-4b9b-b23a-4b7521803527 · outbound

This paper cites VideoControlNet: A Motion-Guided Video-to-Video Translation Framework by Using Diffusion Model with ControlNet.

Noise Consistency Regularization for Improved Subject-Driven Image Synthesis VideoControlNet: A Motion-Guided Video-to-Video Translation Framework by Using Diffusion Model with ControlNet

Reference 21

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Observation bf3d78c4-df18-440d-ba4c-6e885301c058 · outbound

This paper cites Llm-adapters: An adapter family for parameter- efficient fine-tuning of large language models.

Noise Consistency Regularization for Improved Subject-Driven Image Synthesis Llm-adapters: An adapter family for parameter- efficient fine-tuning of large language models

Reference 22

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation c89031fa-0ba1-4d6a-99cf-272bf56156c5 · outbound

This paper cites Auto-encoding vari- ational bayes, 2013.

Noise Consistency Regularization for Improved Subject-Driven Image Synthesis Auto-encoding vari- ational bayes, 2013

Reference 23

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Observation dce3188b-ad25-49f9-84bf-f3f1ba7b2e8f · outbound

This paper cites VeRA: Vector-based Random Matrix Adaptation.

Noise Consistency Regularization for Improved Subject-Driven Image Synthesis VeRA: Vector-based Random Matrix Adaptation

Reference 24

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Observation 9715a0c3-53ab-41a3-82da-be40ba97b6ed · outbound

This paper cites Multi-concept customization of text-to-image diffusion.

Noise Consistency Regularization for Improved Subject-Driven Image Synthesis Multi-concept customization of text-to-image diffusion

Reference 25

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raw_fallback, observed 2026-08-07T05:59:48.491665Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation 82002127-6928-46b5-a137-dc93c7220395 · outbound

This paper cites Direct Consistency Optimization for Robust Customization of Text-to-Image Diffusion Models.

Noise Consistency Regularization for Improved Subject-Driven Image Synthesis Direct Consistency Optimization for Robust Customization of Text-to-Image Diffusion Models

Reference 26

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source=pdf_text observed=2026-08-07T05:59:47.539313Z digest=sha256:42034754db7ae2430351034a75b64a410735c2773269e1332025f88db6c569e1

Observation a6b87d61-1871-4f81-8389-eb44b96c9cee · outbound

This paper cites Parameter-efficient orthogonal finetun- ing via butterfly factorization.

Noise Consistency Regularization for Improved Subject-Driven Image Synthesis Parameter-efficient orthogonal finetun- ing via butterfly factorization

Reference 27

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T05:59:48.478855Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation 8e3a592c-d0e2-4ea0-9952-3af0b1b7e0de · outbound

This paper cites Subject- diffusion: Open domain personalized text-to-image genera- tion without test-time fine-tuning.

Noise Consistency Regularization for Improved Subject-Driven Image Synthesis Subject- diffusion: Open domain personalized text-to-image genera- tion without test-time fine-tuning

Reference 28

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verified fuzzy
raw_fallback, observed 2026-08-07T05:59:48.465141Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation c5011c03-360e-4c34-8bf8-5b80ac659b24 · outbound

This paper cites DiffuseKronA: A Parameter Efficient Fine-tuning Method for Personalized Diffusion Models.

Noise Consistency Regularization for Improved Subject-Driven Image Synthesis DiffuseKronA: A Parameter Efficient Fine-tuning Method for Personalized Diffusion Models

Reference 29

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source=pdf_text observed=2026-08-07T05:59:47.555407Z digest=sha256:4937b09ee2f19a45b38dfbefd2271f88ad3cf86601ea963f94725f42b3c7aaea

Observation 43dbb851-9d82-4a68-b401-284bcc347fbd · outbound

This paper cites Steered diffusion: A generalized framework for plug- and-play conditional image synthesis.

Noise Consistency Regularization for Improved Subject-Driven Image Synthesis Steered diffusion: A generalized framework for plug- and-play conditional image synthesis

Reference 30

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verified fuzzy
raw_fallback, observed 2026-08-07T05:59:48.451319Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation 66d4d4e6-e463-40c4-8911-770c085cc3a5 · outbound

This paper cites Ti2v-zero: Zero-shot image condition- ing for text-to-video diffusion models.

Noise Consistency Regularization for Improved Subject-Driven Image Synthesis Ti2v-zero: Zero-shot image condition- ing for text-to-video diffusion models

Reference 31

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verified fuzzy
raw_fallback, observed 2026-08-07T05:59:48.437827Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T05:59:47.564500Z digest=sha256:30b733b12bcc87863d4cedf6698e27e291b5c3e5f7fb7518e2bc7fb8f5fa0edc

Observation a0a2b3e7-a503-4291-9dc5-c5f93a0439a3 · outbound

This paper cites Nice: Noise-modulated consis- tency regularization for data-efficient gans.Advances in Neu- ral Information Processing Systems, 36:13773–13801, 2023.

Noise Consistency Regularization for Improved Subject-Driven Image Synthesis Nice: Noise-modulated consis- tency regularization for data-efficient gans.Advances in Neu- ral Information Processing Systems, 36:13773–13801, 2023

Reference 32

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raw_fallback, observed 2026-08-07T05:59:48.422802Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T05:59:47.570414Z digest=sha256:1e21b1aa878f8a7f499e619b4567eb5796137b02f4c941baccfc0e66ea6ef928

Observation 1115d579-fc16-41c6-ad7d-9ad832b66958 · outbound

This paper cites Chain: Enhancing generaliza- tion in data-efficient gans via lipschitz continuity constrained normalization.

Noise Consistency Regularization for Improved Subject-Driven Image Synthesis Chain: Enhancing generaliza- tion in data-efficient gans via lipschitz continuity constrained normalization

Reference 33

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raw_fallback, observed 2026-08-07T05:59:48.406813Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T05:59:47.574883Z digest=sha256:282613abb95230a01e76f19aaf42d341dd2585c17256ec278cfc8ac0a6bc814f

Observation 1199218b-d66b-48de-b8df-97e6d7f4ce4c · outbound

This paper cites Cagan: Consistent adversarial training enhanced gans.

Noise Consistency Regularization for Improved Subject-Driven Image Synthesis Cagan: Consistent adversarial training enhanced gans

Reference 34

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raw_fallback, observed 2026-08-07T05:59:48.392163Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T05:59:47.579665Z digest=sha256:3d0fee4315bb54748990f354cb86669fbcb08029a543c9fd245218651abc0b8e

Observation 62d2fe2f-8aaa-4531-92be-294ada58c148 · outbound

This paper cites Pace: Marrying generalization in parameter-efficient fine-tuning with consis- tency regularization.Advances in Neural Information Pro- cessing Systems, 37:61238–61266, 2024.

Noise Consistency Regularization for Improved Subject-Driven Image Synthesis Pace: Marrying generalization in parameter-efficient fine-tuning with consis- tency regularization.Advances in Neural Information Pro- cessing Systems, 37:61238–61266, 2024

Reference 35

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T05:59:48.377554Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T05:59:47.583955Z digest=sha256:fd08d2bd93ee439dedd43f6ca977761a97f3515438a796d9c7d123e1810b94d4

Observation f9e59cad-9aba-4980-ba34-2e1f4097682e · outbound

This paper cites SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis.

Noise Consistency Regularization for Improved Subject-Driven Image Synthesis SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis

Reference 36

Resolution
unresolved
no resolver link, observed 2026-08-07T05:59:47.588336Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T05:59:47.588336Z digest=sha256:db92253dd1eb33b8d5fc65d91f9582c11e3d2b49f20078fc21ad6765c24cfe56

Observation ecccac44-5b47-40d2-88fc-0386c86db5d1 · outbound

This paper cites DreamFusion: Text-to-3D using 2D Diffusion.

Noise Consistency Regularization for Improved Subject-Driven Image Synthesis DreamFusion: Text-to-3D using 2D Diffusion

Reference 37

Resolution
unresolved
no resolver link, observed 2026-08-07T05:59:47.593274Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T05:59:47.593274Z digest=sha256:3be79b83d261ce019de9dcb2c3a2174470c77dedd99ca8ead442855b1ada813c

Observation 7d281d9e-befb-4f11-8b9e-1aada85f8c7a · outbound

This paper cites Controlling text-to-image diffusion by orthogo- nal finetuning.Advances in Neural Information Processing Systems, 36:79320–79362, 2023.

Noise Consistency Regularization for Improved Subject-Driven Image Synthesis Controlling text-to-image diffusion by orthogo- nal finetuning.Advances in Neural Information Processing Systems, 36:79320–79362, 2023

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T05:59:48.363127Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T05:59:47.597917Z digest=sha256:d50148f39a86fe1c032d731da741e6c7b8fe7e95e92e5c1a49345aba1bf232f5

Observation 27e1f51d-7481-47ca-a757-2e44856de93b · outbound

This paper cites Learning transferable visual models from natural language supervi- sion.

Noise Consistency Regularization for Improved Subject-Driven Image Synthesis Learning transferable visual models from natural language supervi- sion

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T05:59:48.348632Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T05:59:47.603333Z digest=sha256:52056bba55580ec9d5a56eaa47332c6c0cbbd4bf87c48fe5fbe9750d31337c92

Observation f204afde-f9ca-4b2a-8411-b89c1859d14c · outbound

This paper cites Direct preference optimization: Your language model is secretly a reward model.Advances in Neural Information Processing Systems, 36:53728–53741, 2023.

Noise Consistency Regularization for Improved Subject-Driven Image Synthesis Direct preference optimization: Your language model is secretly a reward model.Advances in Neural Information Processing Systems, 36:53728–53741, 2023

Reference 40

Resolution
unresolved
no resolver link, observed 2026-08-07T05:59:47.607856Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T05:59:47.607856Z digest=sha256:a6d15c24b4d695c07132ad4262381b93af7c088d1cf4d56748bac063a84d69d5

Observation fb6bd235-c47d-47cf-bbaa-dadd62574001 · outbound

This paper cites Dream- booth3d: Subject-driven text-to-3d generation.

Noise Consistency Regularization for Improved Subject-Driven Image Synthesis Dream- booth3d: Subject-driven text-to-3d generation

Reference 41

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T05:59:48.325681Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T05:59:47.613195Z digest=sha256:14a1f2c5fa2419dcc614e7e0b9f8921a9ceecf108c177ccd5a6b26e4033c2cb9

Observation 933e9fe5-466d-4149-8a93-51090ba973e7 · outbound

This paper cites Zero-shot text-to-image generation.

Noise Consistency Regularization for Improved Subject-Driven Image Synthesis Zero-shot text-to-image generation

Reference 42

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T05:59:48.311385Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T05:59:47.617599Z digest=sha256:e8160dd859ea1a9b823336f002bd4784950b1e9a88d20ab4d0702870b7583346

Observation 7c2c6ec2-3ecf-4c80-9628-54de25205f08 · outbound

This paper cites Hierarchical Text-Conditional Image Generation with CLIP Latents.

Noise Consistency Regularization for Improved Subject-Driven Image Synthesis Hierarchical Text-Conditional Image Generation with CLIP Latents

Reference 43

Resolution
unresolved
no resolver link, observed 2026-08-07T05:59:47.622028Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T05:59:47.622028Z digest=sha256:f6a7106954eae61fef4b3471ecc17adba94a724d6b17d029aa465b488201c66d

Observation d31bdb7d-1e78-4ddf-b025-a14b39206d4e · outbound

This paper cites Learning multiple visual domains with residual adapters.Ad- vances in neural information processing systems, 30, 2017.

Noise Consistency Regularization for Improved Subject-Driven Image Synthesis Learning multiple visual domains with residual adapters.Ad- vances in neural information processing systems, 30, 2017

Reference 44

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T05:59:48.295570Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T05:59:47.627084Z digest=sha256:c101634e9f6336e8ad4975e6e08d6404fd47800ef8b13e9fdd3f4f63683bf1e7

Observation e5855865-9019-47ab-be17-35b38ae9a683 · outbound

This paper cites High-resolution image synthesis with latent diffusion models.

Noise Consistency Regularization for Improved Subject-Driven Image Synthesis High-resolution image synthesis with latent diffusion models

Reference 45

Resolution
unresolved
no resolver link, observed 2026-08-07T05:59:47.631417Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T05:59:47.631417Z digest=sha256:5a994cd240365e2d171acd3e0bc5ddc01db682eb0a07420c740e1ae036f8ebf2

Observation 91fd4e7e-8b7f-4ead-bae5-b90b332c71fe · outbound

This paper cites Consistency-guided prompt learning for vision-language models.

Noise Consistency Regularization for Improved Subject-Driven Image Synthesis Consistency-guided prompt learning for vision-language models

Reference 46

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T05:59:48.270189Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T05:59:47.636456Z digest=sha256:4a80c9bbd9557be9ace2e54e3a315d330cd91119b38f8502c196139fe0575731

Observation 76718729-7538-44ec-a385-74c60e59ba9a · outbound

This paper cites Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation.

Noise Consistency Regularization for Improved Subject-Driven Image Synthesis Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation

Reference 47

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T05:59:48.253582Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T05:59:47.640725Z digest=sha256:1d070e2094edeb8b9e1e497c72b3d32439cbb99955fcc1001294799ba5b858c4

Observation 98af75e6-18b7-45d0-83f9-c0be207bd870 · outbound

This paper cites Hyperdreambooth: Hypernetworks for fast personalization of text-to-image models.

Noise Consistency Regularization for Improved Subject-Driven Image Synthesis Hyperdreambooth: Hypernetworks for fast personalization of text-to-image models

Reference 48

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T05:59:48.236982Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T05:59:47.645642Z digest=sha256:653d66f578cec811dfd76ef9ec708a94a67706c29e6ff0d2e56559570387755c

Observation 130a1c57-ee90-44c1-a376-ee32a3fd3189 · outbound

This paper cites Photorealistic text-to-image diffusion models with deep language understanding.Advances in Neural Information Processing Systems, 35:36479–36494, 2022.

Noise Consistency Regularization for Improved Subject-Driven Image Synthesis Photorealistic text-to-image diffusion models with deep language understanding.Advances in Neural Information Processing Systems, 35:36479–36494, 2022

Reference 49

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T05:59:48.221749Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T05:59:47.650498Z digest=sha256:06639d225446eac414cf95ed9e32cb36f4dd00742a532e594879ff468acb8457

Observation 16e5bb11-c0da-46fb-8677-c608cf4748ab · outbound

This paper cites Open- match: Open-set semi-supervised learning with open-set consistency regularization.Advances in Neural Information Processing Systems, 34:25956–25967, 2021.

Noise Consistency Regularization for Improved Subject-Driven Image Synthesis Open- match: Open-set semi-supervised learning with open-set consistency regularization.Advances in Neural Information Processing Systems, 34:25956–25967, 2021

Reference 50

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T05:59:48.207280Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T05:59:47.654613Z digest=sha256:ef1c08a5e5bd0fb5f2b1055c9b14cfa64f674cd32ceea74e0d34c20029e988eb

Observation c23a2461-98cc-4973-abbe-730346a51bf4 · outbound

This paper cites Laion-5b: An open large-scale dataset for training next generation image-text models.Advances in Neural In- formation Processing Systems, 35:25278–25294, 2022.

Noise Consistency Regularization for Improved Subject-Driven Image Synthesis Laion-5b: An open large-scale dataset for training next generation image-text models.Advances in Neural In- formation Processing Systems, 35:25278–25294, 2022

Reference 51

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T05:59:48.192686Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T05:59:47.659313Z digest=sha256:330a8e753510fa8bff8129102b701384f2c11afc6ccdf02b660938810dbd71ea

Observation 51c41672-6ad8-4e2b-b832-947fadadcec1 · outbound

This paper cites Instant- booth: Personalized text-to-image generation without test- time finetuning.

Noise Consistency Regularization for Improved Subject-Driven Image Synthesis Instant- booth: Personalized text-to-image generation without test- time finetuning

Reference 52

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T05:59:48.178694Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T05:59:47.664348Z digest=sha256:0d8b5222224f41f20d6ecb5b27bca4eb4ec60fd26554fc36c7e309bdbb6d57be

Observation edc94454-b3e4-4fb4-938f-9f531f76c507 · outbound

This paper cites Fixmatch: Simplifying semi-supervised learning with consistency and confidence.

Noise Consistency Regularization for Improved Subject-Driven Image Synthesis Fixmatch: Simplifying semi-supervised learning with consistency and confidence

Reference 53

Resolution
unresolved
no resolver link, observed 2026-08-07T05:59:47.669088Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T05:59:47.669088Z digest=sha256:64c9afe8bda3acba72797769e558630ef7de62c5d500878cab933e244f2abe91

Observation 9009ae3c-fbf3-48c1-83d8-3035d038ea01 · outbound

This paper cites StyleDrop: Text-to-Image Generation in Any Style.

Noise Consistency Regularization for Improved Subject-Driven Image Synthesis StyleDrop: Text-to-Image Generation in Any Style

Reference 54

Resolution
unresolved
no resolver link, observed 2026-08-07T05:59:47.673799Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T05:59:47.673799Z digest=sha256:662cc39c339b0ccd28f6858e4d769c2ca66a9c8960c88f8416af564e971f9d28

Observation 9bd3cff8-9583-4ff7-9122-50349746884f · outbound

This paper cites Key-locked rank one editing for text-to-image personaliza- tion.

Noise Consistency Regularization for Improved Subject-Driven Image Synthesis Key-locked rank one editing for text-to-image personaliza- tion

Reference 55

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T05:59:48.153945Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T05:59:47.679198Z digest=sha256:97589d9e7b5e7d65fa3f2e52aa2ebcdeb69cd4fd90da05623270a24ca159fb34

Observation 7626ef10-0b0f-4890-9c21-650c2739d493 · outbound

This paper cites Attention is all you need.

Noise Consistency Regularization for Improved Subject-Driven Image Synthesis Attention is all you need

Reference 56

Resolution
unresolved
no resolver link, observed 2026-08-07T05:59:47.683454Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T05:59:47.683454Z digest=sha256:0ed02f69c5bb21dc53318db350f4b6cfe314f7523ec32c09cf555c3db07bab2b

Observation 16f26ded-95af-43c2-8e65-b210dd6a9b64 · outbound

This paper cites P+: Extended Textual Conditioning in Text-to-Image Generation.

Noise Consistency Regularization for Improved Subject-Driven Image Synthesis P+: Extended Textual Conditioning in Text-to-Image Generation

Reference 57

Resolution
unresolved
no resolver link, observed 2026-08-07T05:59:47.689532Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T05:59:47.689532Z digest=sha256:89c9dcb02eb3edb08fcc28b5ecd42b7063366ea61bd6e54d1521df149e2c4232

Observation 2b5d1dea-4b38-43dc-9066-fc212e2d4c4a · outbound

This paper cites Prolificdreamer: High-fidelity and diverse text-to-3d generation with variational score distilla- tion.Advances in Neural Information Processing Systems, 36, 2024.

Noise Consistency Regularization for Improved Subject-Driven Image Synthesis Prolificdreamer: High-fidelity and diverse text-to-3d generation with variational score distilla- tion.Advances in Neural Information Processing Systems, 36, 2024

Reference 58

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T05:59:48.129754Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T05:59:47.694195Z digest=sha256:fc1aa0d0cd283ce9884cce2fa0d2c3195d305c2bf2776a2c3cd3b1c466d5fdb3

Observation 5a7313d0-0483-434a-926e-f7fa5e92f9f7 · outbound

This paper cites Elite: Encoding visual con- cepts into textual embeddings for customized text-to-image generation.

Noise Consistency Regularization for Improved Subject-Driven Image Synthesis Elite: Encoding visual con- cepts into textual embeddings for customized text-to-image generation

Reference 59

Resolution
unresolved
no resolver link, observed 2026-08-07T05:59:47.700103Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T05:59:47.700103Z digest=sha256:3583a2d6d6bc701299a4e20a4a00a7d17acbfc02d47ec96679df346f6fbc3121

Observation 415a55ef-4ac2-4f37-a731-63cb73c07d42 · outbound

This paper cites R-drop: Regularized dropout for neural networks.Advances in Neural Informa- tion Processing Systems, 34:10890–10905, 2021.

Noise Consistency Regularization for Improved Subject-Driven Image Synthesis R-drop: Regularized dropout for neural networks.Advances in Neural Informa- tion Processing Systems, 34:10890–10905, 2021

Reference 60

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T05:59:48.106310Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T05:59:47.704465Z digest=sha256:c969cd683d8215c2689f8c9410130c108cc75bfd77304da21547f12b7c73eaad

Observation 1183b934-5df5-4f4b-b1fe-5b07ffb966fe · outbound

This paper cites Infinite-id: Identity-preserved personalization via id- semantics decoupling paradigm.

Noise Consistency Regularization for Improved Subject-Driven Image Synthesis Infinite-id: Identity-preserved personalization via id- semantics decoupling paradigm

Reference 61

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T05:59:48.090037Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T05:59:47.709011Z digest=sha256:2a72a0ef84d251430a46f34896e0e8a4579d825a64fb26da24b28995b6042e76

Observation 967c3591-1f17-4d4d-896e-6b57b5014518 · outbound

This paper cites IP-Adapter: Text Compatible Image Prompt Adapter for Text-to-Image Diffusion Models.

Noise Consistency Regularization for Improved Subject-Driven Image Synthesis IP-Adapter: Text Compatible Image Prompt Adapter for Text-to-Image Diffusion Models

Reference 62

Resolution
unresolved
no resolver link, observed 2026-08-07T05:59:47.714248Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T05:59:47.714248Z digest=sha256:56cb128df36f4ee1236b86731de9260ec685fbdae841482f6860f1ce366c0171

Observation 7b920ddb-3658-48f7-af95-444c3919e355 · outbound

This paper cites Consistency Regularization for Generative Adversarial Networks.

Noise Consistency Regularization for Improved Subject-Driven Image Synthesis Consistency Regularization for Generative Adversarial Networks

Reference 63

Resolution
unresolved
no resolver link, observed 2026-08-07T05:59:47.719084Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T05:59:47.719084Z digest=sha256:baaea06a58257639b6e8313e3fd5b9b68415473c5cffb2ce00f98e7435a51087

Observation b08323ab-8954-42e7-89bf-241f38e7ac6c · outbound

This paper cites Adding conditional control to text-to-image diffusion models.

Noise Consistency Regularization for Improved Subject-Driven Image Synthesis Adding conditional control to text-to-image diffusion models

Reference 64

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T05:59:48.075779Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T05:59:47.724173Z digest=sha256:5fcd082460cd599e4059c950f7b81d1140f9d5c21f35cdc451efb3d2c3c95dc2

Observation 1ec73185-e355-46fa-85a5-143bae6c3cb7 · outbound

This paper cites Adaptive budget allocation for parameter-efficient fine- tuning.

Noise Consistency Regularization for Improved Subject-Driven Image Synthesis Adaptive budget allocation for parameter-efficient fine- tuning

Reference 65

Resolution
unresolved
no resolver link, observed 2026-08-07T05:59:47.728993Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T05:59:47.728993Z digest=sha256:af528252f016e6ba634db66e947e4604c8ab669fb1a39e28f0525e12e132cbf9

Observation 28cdec69-1195-4131-9a48-5a38190abf8d · outbound

This paper cites Spectrum-Aware Parameter Efficient Fine-Tuning for Diffusion Models.

Noise Consistency Regularization for Improved Subject-Driven Image Synthesis Spectrum-Aware Parameter Efficient Fine-Tuning for Diffusion Models

Reference 66

Resolution
verified exact
local_arxiv, observed 2026-08-07T05:59:47.804429Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T05:59:47.733583Z digest=sha256:4ba8165e15b7928c095931c60fee428f699da2f6a9683566a06dc3dedb0a5a7f

Observation dbaaff36-036a-414e-9a53-26e317bd124c · outbound

This paper cites Uni-controlnet: All-in-one control to text-to-image diffusion models.Advances in Neural Information Processing Sys- tems, 36, 2024.

Noise Consistency Regularization for Improved Subject-Driven Image Synthesis Uni-controlnet: All-in-one control to text-to-image diffusion models.Advances in Neural Information Processing Sys- tems, 36, 2024

Reference 67

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T05:59:48.051262Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T05:59:47.738750Z digest=sha256:6ded60cd919563567b82ed8359a2744fb2df32b5d74370bf7bb5bc1af8b3e4e4

Observation 1f4d73c7-91ce-42cc-af58-0664cca204aa · outbound

This paper cites Asymmetry in Low-Rank Adapters of Foundation Models.

Noise Consistency Regularization for Improved Subject-Driven Image Synthesis Asymmetry in Low-Rank Adapters of Foundation Models

Reference 68

Resolution
unresolved
no resolver link, observed 2026-08-07T05:59:47.743134Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T05:59:47.743134Z digest=sha256:6096fdf821b3456a089893b02b2431c730fe002c2fb030d7921a736933f3f080

Pith citing papers

No inbound Pith citation observations are available.